Presence of Flavivirus Antibodies Does Not Lead to a Greater Number of Symptoms in a Small Cohort of Canadian Travelers Infected with Zika Virus
Bibliographic record
Abstract
Zika virus (ZIKV) is a mosquito-borne flavivirus associated with a febrile illness as well as severe complications, including microcephaly and Guillain-Barré Syndrome. Antibody cross-reactivity between flaviviruses has been documented, and in regions where ZIKV is circulating, dengue virus (DENV) is also endemic, leaving the potential that previous exposure to DENV could alter clinical features of ZIKV infection. To investigate this, we performed a retrospective case-control study in which we compared Canadian travellers who had been infected with ZIKV and had serological findings indicating previous DENV or other flavivirus exposure (n = 16) to those without any previous exposure (n = 44). Patient samples were collected between February 2016 and September 2017 and submitted to Public Health Ontario for testing. ZIKV infection was determined using real-time RT-PCR and antibodies against DENV were identified by the plaque-reduction neutralization test. The mean time from symptom onset to sample collection was 5 days for both groups; the magnitude of viremia was not statistically different (Ct values: 35.6 vs. 34.9, p-value = 0.2). Clinical scores were also similar. Our findings indicate that previous DENV or other flavivirus exposure did not result in greater viremia or a higher illness score.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".